ANALISIS HUBUNGAN INDEKS PEMBANGUNAN MANUSIA, JUMLAH PENDUDUK, DAN PEREKONOMIAN TERHADAP TIMBULAN SAMPAH DI SULAWESI UTARA
Bibliographic record
Abstract
Municipal solid waste generation at the regency level has become an increasingly pressing issue, driven by the growth of social and economic activities. This study aims to examine the relationship between regional development factors and the volume of waste produced across 15 regencies in North Sulawesi. Using a quantitative associative framework, the analysis draws on panel data from 2022 to 2024 and is conducted using a Random Effects regression model with a between-effects estimation method. The dependent variable in this study is waste generation, while the independent variables consist of Human Development Index (HDI), Gross Regional Domestic Product (GRDP) and population. The results indicate that both HDI and population are positively and significantly associated with waste generation across regions, whereas Gross Regional Domestic Product shows no statistically significant relationship. These findings suggest that regions with higher levels of welfare and larger populations tend to generate more waste. However, the between-effects approach is limited, as it reflects only cross-sectional variation and does not account for temporal dynamics or support direct conclusions about causality. As such, the interpretations are correlational, reflecting structural differences across regions rather than dynamic developments. This study provides meaningful insights for urban and regional planning, particularly in guiding the development of waste management policies that align with the socio-economic conditions of each region. ABSTRAKPermasalahan timbulan sampah di tingkat kabupaten/kota semakin kompleks seiring meningkatnya aktivitas sosial dan ekonomi masyarakat. Studi ini bertujuan untuk mengidentifikasi hubungan antara karakteristik pembangunan wilayah dan volume timbulan sampah di 15 kabupaten/kota di Provinsi Sulawesi Utara. Penelitian ini menggunakan pendekatan kuantitatif asosiatif dengan data panel periode 2022–2024, yang dianalisis menggunakan regresi Random Effects dengan pendekatan between-effects. Variabel dependen dalam penelitian ini adalah timbulan sampah, sementara variabel independen terdiri dari Indeks Pembangunan Manusia (IPM), Produk Domestik Regional Bruto (PDRB) dan Jumlah Penduduk. Hasil analisis menunjukkan bahwa IPM dan Jumlah Penduduk memiliki hubungan positif dan signifikan secara statistik terhadap timbulan sampah antar wilayah, sedangkan PDRB tidak menunjukkan hubungan yang signifikan. Temuan ini menegaskan bahwa daerah dengan tingkat kesejahteraan dan populasi yang lebih tinggi cenderung menghasilkan timbulan sampah yang lebih besar. Namun, pendekatan model antara-wilayah ini tidak dapat menjelaskan perubahan dari waktu ke waktu maupun hubungan kausal secara langsung. Interpretasi hasil bersifat korelasional dan mencerminkan kondisi struktural antar daerah. Penelitian ini memberikan kontribusi penting dalam kajian perencanaan wilayah dan kota, khususnya dalam penyusunan kebijakan pengelolaan sampah yang berbasis pada karakteristik sosial-ekonomi wilayah.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".